IBM's HyRef Seeks to Solve Wind's Intermittency Problem

New Hampshire, USA --
IBM has decloaked a system combining weather modeling, sensors, and analytics to better forecast a wind farm's output from minutes to weeks away, helping utilities and grid operators more confidently integrate an intermittent energy source.

IBM's "Hybrid Renewable Energy Forecasting" (HyRef) system combines weather modeling capabilities, sky-gazing cameras and imaging technology, sensors on the turbines, and sorts it all through analytics software to predict incoming weather patterns and calculate wind turbine performance, from 15-minute intervals up to 30 days in advance. The upshot is more efficiency and reliability from wind farms, and reduced intermittency of delivery into the grid.

Michael Valocchi, VP in IBM's Global Energy and Utilities Industry business, talked us through the HyRef's origins and applications. Essentially HyRef folds in several key components:

Weather modeling capabilities that have been fine-tuned "to a much more granular basis," from a smaller area perspective within a square kilometer to vertical heights where turbine hubs and rotors are located;

Sensors on the turbines to monitor wind speed, turbulence, temperature, and direction; and

Analytics capabilities to collect and manage the data, both structured and "unstructured" (data that can't be natively stored in a spreadsheet; images, possibly multimedia, etc.). SAS provides the backbone for this, on a DB2 platform.

Here's an infographic explaining all that in pleasing pictorial form. And here's a video for a more interactive view of the wind intermittency problem and HyRef's solution.

Finally, check out the Image Gallery at right to see a customer's dashboard view of data.

HyRef had its origins in a late-1990s project dubbed "Deep Thunder" to produce weather modeling and forecasts to anticipate major weather events. Two years ago the work took a different turn when Vestas hired IBM to help optimize wind turbine placement at facilities in Denmark, analyzing "petabytes of data" -- weather reports, tidal phases, geospatial and sensor data, satellite images, deforestation maps, and weather modeling -- to shave the process from several weeks to less than an hour, according to Valocchi.

Fast-forward to today, and those efforts in weather forecasting and analytics are intersecting to provide "a more holistic prediction capability" for a wind farm's operation and output, Valocchi explains. HyRef "looks at the output of the set of turbines -- not just 'is it cloudy?' or 'is it windy today?' -- pulling together thousands of data points to come to that level of predictability," he said. "It's not just a high-level weather forecast."

Those other efforts, however, are still in the initial testing stages or being developed over the next couple of years, and in the case of NCAR/Xcel aiming for "probabilistic" estimates of weather events anywhere from 20-80 percent. Meanwhile, IBM's HyRef is running right now at a customer site: the Chinese State Grid Corp.'s 670-MW Zhangbei "demonstration" project, home to 500 MW of wind, on top of 100-MW of solar and a 70-MW battery storage system. And it's proven to deliver 92 percent accuracy.

Here a snapshot of Zhangbei's HyRef dashboard, provided by IBM. Again, more screenshots are in the image gallery, see above & to the right.

Zhangbei also wants to apply the same forecasting abilities to its solar output; "obviously there are different things to look at" with solar forecasting, he noted, "but absolutely it's meant for not just wind."

HyRef, IBM claims, is helping the Zhangbei project better manage its wind output, reduce the need for curtailment, and increase the integration of renewable power generation by 10 percent, according to IBM. "It's better operations from a grid perspective, increasing output, better predictability," Velocchi said.

I have to say, I don't like the angle in the article. So IBM is running this "right now at a customer site", while the competitors only have stuff "in the initial testing stages or being developed over the next couple of years". This does not do the competition justice, as for example probablistic forecasts have run since the 1990ies in Denmark and Germany operatively, and solar forecasts and forecasts based on offsite observations since a number of years too.
The number of "92% accuracy" is not very helpful either, as I fail to see how it is calculated.

Having said that, the approach as such sounds interesting, and I would like to see some more scientific accounts of the system and the performance. A single wind farm is not quite enough for good statistics on performance...

I have to say, I don't like the angle in the article. So IBM is running this "right now at a customer site", while the competitors only have stuff "in the initial testing stages or being developed over the next couple of years". This does not do the competition justice, as for example probablistic forecasts have run since the 1990ies in Denmark and Germany operatively, and solar forecasts and forecasts based on offsite observations since a number of years too.
The number of "92% accuracy" is not very helpful either, as I fail to see how it is calculated.

Having said that, the approach as such sounds interesting, and I would like to see some more scientific accounts of the system and the performance. A single wind farm is not quite enough for good statistics on performance...

Our understanding is that GreenNH3 is now used as storage of wind energy.
No more need to predict, just store every jewel of energy for when you need it.
Believe I saw they where now using it or about to use it in Denmark. Cheers.

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Jim is Contributing Editor for RenewableEnergyWorld.com, covering the solar and wind beats. He previously was associate editor for Solid State Technology and Photovoltaics World, and has covered semiconductor manufacturing and related industries,...